New Frontiers in Applied Data Mining: PAKDD 2009 International Workshops, Bangkok, Thailand, April 27-30, 2010. Revised Selected Papers(Lecture Notes in Artificial Intelligence)

Five high-quality workshops were held at the 13th Paci?c-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009) in Bangkok, Thailand during April 27-30, 2009. There were 17, 6, 9, 4 and 5 accepted papers to be presented at the Paci?c Asia Workshop on Intelligence and Security Informatics (PAISI 2009), the workshop on Advances and Issues in Biomedical Data Mining (AIBDM 2009), the workshop on Data Mining with Imbalanced Classes and Error Cost (ICEC 2009),the workshopon Open Source in Data Mining (OSDM 2009), and the workshop on Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE 2009). One competition, PAKDD 2009 Data Mining Competition, and one local workshop, Thai Track Session, were arranged. From these workshops (except PAISI which published its works in separate LNCS proceedings), we selected two or three best papers for this LNCS publication. PAKDD is a major international conference in the areas of data mining (DM) and knowledge discovery in database (KDD). It provides an internationalforum for researchersand industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scienti?c discovery, data visualization, causal induction and knowledge-based systems. In general,we wish to thank our General WorkshopCo-chairs,Manabu O- mura and Bernhard Pfahringe, for selecting and coordinating the great wo- shops. WewouldliketothankJunbinGao(CharlesSturtUniversity),PaulKwan (UniversityofNewEngland,Australia),JosiahPoon(UniversityofSydney),and Simon Poon (University of Sydney), for their arrangement of AIBDM 2009.

Table of Contents:
The iZi Project: Easy Prototyping of Interesting Pattern Mining Algorithms.- CODE: A Data Complexity Framework for Imbalanced Datasets.- An Empirical Study of Applying Ensembles of Heterogeneous Classifiers on Imperfect Data.- Undersampling Approach for Imbalanced Training Sets and Induction from Multi-label Text-Categorization Domains.- Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams.- Two Measures of Objective Novelty in Association Rule Mining.- PAKDD Data Mining Competition 2009: New Ways of Using Known Methods.- Feature Selection for Brain-Computer Interfaces.- Mining Protein Interactions from Text Using Convolution Kernels.- Missing Phrase Recovering by Combining Forward and Backward Phrase Translation Tables.- Automatic Extraction of Thai-English Term Translations and Synonyms from Medical Web using Iterative Candidate Generation with Association Measures.- Accurate Subsequence Matching on Data Stream under Time Warping Distance.